Hi all, I'm curious about MLlib and if it is possible to do incremental training on the ALSModel.
Usually training is run first, and then you can query. But in my case, data is collected in real-time and I want the predictions of my ALSModel to consider the latest data without complete re-training phase. I've checked out these resources, but could not find any info on how to solve this: https://spark.apache.org/docs/latest/mllib-collaborative-filtering.html http://ampcamp.berkeley.edu/big-data-mini-course/movie-recommendation-with-mllib.html My question fits in a larger picture where I'm using Prediction IO, and this in turn is based on Spark. Thanks in advance for any advice! Wouter -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Is-it-possible-to-do-incremental-training-using-ALSModel-MLlib-tp20942.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
